Add Demographic data analyzer

9-data-analysis-python/2-demographic-data-analyzer/README.md

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+# Demographic Data Analyzer
+
+This is the boilerplate for the Demographic Data Analyzer project. Instructions for building your project can be found at https://www.freecodecamp.org/learn/data-analysis-with-python/data-analysis-with-python-projects/demographic-data-analyzer

9-data-analysis-python/2-demographic-data-analyzer/demographic_data_analyzer.py

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+import pandas as pd
+
+
+def calculate_demographic_data(print_data=True):
+    # Read data from file
+    df = pd.read_csv("adult.data.csv")
+
+    # How many of each race are represented in this dataset? This should be a Pandas series with race names as the index labels.
+    race_count = df["race"].value_counts()
+
+    # What is the average age of men?
+    average_age_men = df.loc[df['sex'] == 'Male']['age'].mean()
+
+    # What is the percentage of people who have a Bachelor's degree?
+    percentage_bachelors = df.loc[df['education'] == 'Bachelors'].size / df.size * 100
+
+    # What percentage of people with advanced education (`Bachelors`, `Masters`, or `Doctorate`) make more than 50K?
+    # What percentage of people without advanced education make more than 50K?
+
+    # with and without `Bachelors`, `Masters`, or `Doctorate`
+    higher_education = df.loc[(df['education'] == 'Bachelors') | (df['education'] == 'Masters') | (df['education'] == 'Doctorate')]
+    lower_education = df.loc[(df['education'] != 'Bachelors') & (df['education'] != 'Masters') & (df['education'] != 'Doctorate')]
+
+    # percentage with salary >50K
+    higher_education_rich = higher_education.loc[higher_education['salary'] == ">50K"].size / higher_education.size * 100
+    lower_education_rich = lower_education.loc[lower_education['salary'] == ">50K"].size / lower_education.size * 100
+
+    # What is the minimum number of hours a person works per week (hours-per-week feature)?
+    min_work_hours = df['hours-per-week'].min()
+
+    # What percentage of the people who work the minimum number of hours per week have a salary of >50K?
+    num_min_workers = df.loc[df['hours-per-week'] == min_work_hours]
+
+    rich_percentage = num_min_workers.loc[num_min_workers['salary'] == ">50K"].size / num_min_workers.size * 100
+
+    # What country has the highest percentage of people that earn >50K?
+    highest_earning_country = df.loc[df['salary'] == ">50K"]['native-country'].value_counts().idxmax()
+    highest_earning_country_percentage = df.loc[(df['salary'] == ">50K") & (df['native-country'] == highest_earning_country)].size / df.loc[df['native-country'] == highest_earning_country].size * 100
+
+    # Identify the most popular occupation for those who earn >50K in India.
+    top_IN_occupation = df.loc[(df['salary'] == ">50K") & (df['native-country'] == 'India')]['occupation'].value_counts().idxmax()
+
+    # DO NOT MODIFY BELOW THIS LINE
+
+    if print_data:
+        print("Number of each race:\n", race_count) 
+        print("Average age of men:", average_age_men)
+        print(f"Percentage with Bachelors degrees: {percentage_bachelors}%")
+        print(f"Percentage with higher education that earn >50K: {higher_education_rich}%")
+        print(f"Percentage without higher education that earn >50K: {lower_education_rich}%")
+        print(f"Min work time: {min_work_hours} hours/week")
+        print(f"Percentage of rich among those who work fewest hours: {rich_percentage}%")
+        print("Country with highest percentage of rich:", highest_earning_country)
+        print(f"Highest percentage of rich people in country: {highest_earning_country_percentage}%")
+        print("Top occupations in India:", top_IN_occupation)
+
+    return {
+        'race_count': race_count,
+        'average_age_men': average_age_men,
+        'percentage_bachelors': percentage_bachelors,
+        'higher_education_rich': higher_education_rich,
+        'lower_education_rich': lower_education_rich,
+        'min_work_hours': min_work_hours,
+        'rich_percentage': rich_percentage,
+        'highest_earning_country': highest_earning_country,
+        'highest_earning_country_percentage':
+        highest_earning_country_percentage,
+        'top_IN_occupation': top_IN_occupation
+    }

9-data-analysis-python/2-demographic-data-analyzer/main.py

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+# This entrypoint file to be used in development. Start by reading README.md
+import demographic_data_analyzer
+from unittest import main
+
+# Test your function by calling it here
+demographic_data_analyzer.calculate_demographic_data()
+
+# Run unit tests automatically
+#main(module='test_module', exit=False)

9-data-analysis-python/2-demographic-data-analyzer/poetry.lock

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+[[package]]
+category = "main"
+description = "NumPy is the fundamental package for array computing with Python."
+name = "numpy"
+optional = false
+python-versions = ">=3.5"
+version = "1.18.5"
+
+[[package]]
+category = "main"
+description = "Powerful data structures for data analysis, time series, and statistics"
+name = "pandas"
+optional = false
+python-versions = ">=3.6.1"
+version = "1.0.4"
+
+[package.dependencies]
+numpy = ">=1.13.3"
+python-dateutil = ">=2.6.1"
+pytz = ">=2017.2"
+
+[[package]]
+category = "main"
+description = "Extensions to the standard Python datetime module"
+name = "python-dateutil"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+version = "2.8.1"
+
+[package.dependencies]
+six = ">=1.5"
+
+[[package]]
+category = "main"
+description = "World timezone definitions, modern and historical"
+name = "pytz"
+optional = false
+python-versions = "*"
+version = "2020.1"
+
+[[package]]
+category = "main"
+description = "Python 2 and 3 compatibility utilities"
+name = "six"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
+version = "1.15.0"
+
+[metadata]
+content-hash = "27114271cf207dff3920111c8aa89baba75353cc23851aded0a93b193dc24770"
+python-versions = "^3.8"
+
+[metadata.hashes]
+numpy = ["0172304e7d8d40e9e49553901903dc5f5a49a703363ed756796f5808a06fc233", "34e96e9dae65c4839bd80012023aadd6ee2ccb73ce7fdf3074c62f301e63120b", "3676abe3d621fc467c4c1469ee11e395c82b2d6b5463a9454e37fe9da07cd0d7", "3dd6823d3e04b5f223e3e265b4a1eae15f104f4366edd409e5a5e413a98f911f", "4064f53d4cce69e9ac613256dc2162e56f20a4e2d2086b1956dd2fcf77b7fac5", "4674f7d27a6c1c52a4d1aa5f0881f1eff840d2206989bae6acb1c7668c02ebfb", "7d42ab8cedd175b5ebcb39b5208b25ba104842489ed59fbb29356f671ac93583", "965df25449305092b23d5145b9bdaeb0149b6e41a77a7d728b1644b3c99277c1", "9c9d6531bc1886454f44aa8f809268bc481295cf9740827254f53c30104f074a", "a78e438db8ec26d5d9d0e584b27ef25c7afa5a182d1bf4d05e313d2d6d515271", "a7acefddf994af1aeba05bbbafe4ba983a187079f125146dc5859e6d817df824", "a87f59508c2b7ceb8631c20630118cc546f1f815e034193dc72390db038a5cb3", "ac792b385d81151bae2a5a8adb2b88261ceb4976dbfaaad9ce3a200e036753dc", "b03b2c0badeb606d1232e5f78852c102c0a7989d3a534b3129e7856a52f3d161", "b39321f1a74d1f9183bf1638a745b4fd6fe80efbb1f6b32b932a588b4bc7695f", "cae14a01a159b1ed91a324722d746523ec757357260c6804d11d6147a9e53e3f", "cd49930af1d1e49a812d987c2620ee63965b619257bd76eaaa95870ca08837cf", "e15b382603c58f24265c9c931c9a45eebf44fe2e6b4eaedbb0d025ab3255228b", "e91d31b34fc7c2c8f756b4e902f901f856ae53a93399368d9a0dc7be17ed2ca0", "ef627986941b5edd1ed74ba89ca43196ed197f1a206a3f18cc9faf2fb84fd675", "f718a7949d1c4f622ff548c572e0c03440b49b9531ff00e4ed5738b459f011e8"]
+pandas = ["034185bb615dc96d08fa13aacba8862949db19d5e7804d6ee242d086f07bcc46", "0c9b7f1933e3226cc16129cf2093338d63ace5c85db7c9588e3e1ac5c1937ad5", "1f6fcf0404626ca0475715da045a878c7062ed39bc859afc4ccf0ba0a586a0aa", "1fc963ba33c299973e92d45466e576d11f28611f3549469aec4a35658ef9f4cc", "29b4cfee5df2bc885607b8f016e901e63df7ffc8f00209000471778f46cc6678", "2a8b6c28607e3f3c344fe3e9b3cd76d2bf9f59bc8c0f2e582e3728b80e1786dc", "2bc2ff52091a6ac481cc75d514f06227dc1b10887df1eb72d535475e7b825e31", "415e4d52fcfd68c3d8f1851cef4d947399232741cc994c8f6aa5e6a9f2e4b1d8", "519678882fd0587410ece91e3ff7f73ad6ded60f6fcb8aa7bcc85c1dc20ecac6", "51e0abe6e9f5096d246232b461649b0aa627f46de8f6344597ca908f2240cbaa", "698e26372dba93f3aeb09cd7da2bb6dd6ade248338cfe423792c07116297f8f4", "83af85c8e539a7876d23b78433d90f6a0e8aa913e37320785cf3888c946ee874", "982cda36d1773076a415ec62766b3c0a21cdbae84525135bdb8f460c489bb5dd", "a647e44ba1b3344ebc5991c8aafeb7cca2b930010923657a273b41d86ae225c4", "b35d625282baa7b51e82e52622c300a1ca9f786711b2af7cbe64f1e6831f4126", "bab51855f8b318ef39c2af2c11095f45a10b74cbab4e3c8199efcc5af314c648"]
+python-dateutil = ["73ebfe9dbf22e832286dafa60473e4cd239f8592f699aa5adaf10050e6e1823c", "75bb3f31ea686f1197762692a9ee6a7550b59fc6ca3a1f4b5d7e32fb98e2da2a"]
+pytz = ["a494d53b6d39c3c6e44c3bec237336e14305e4f29bbf800b599253057fbb79ed", "c35965d010ce31b23eeb663ed3cc8c906275d6be1a34393a1d73a41febf4a048"]
+six = ["30639c035cdb23534cd4aa2dd52c3bf48f06e5f4a941509c8bafd8ce11080259", "8b74bedcbbbaca38ff6d7491d76f2b06b3592611af620f8426e82dddb04a5ced"]

9-data-analysis-python/2-demographic-data-analyzer/pyproject.toml

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+[tool.poetry]
+name = "fcc-demographic-data-analyzer"
+version = "0.1.0"
+description = ""
+authors = ["Your Name <you@example.com>"]
+
+[tool.poetry.dependencies]
+python = "^3.8"
+pandas = "^1.0"
+
+[tool.poetry.dev-dependencies]
+
+[build-system]
+requires = ["poetry>=0.12"]
+build-backend = "poetry.masonry.api"

9-data-analysis-python/2-demographic-data-analyzer/renovate.json

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+{
+  "labels": ["renovate"],
+  "extends": ["config:base"],
+  "branchConcurrentLimit": 20,
+  "dependencyDashboard": true,
+  "major": {
+    "dependencyDashboardApproval": true
+  },
+  "packageRules": [
+    {
+      "matchUpdateTypes": ["minor", "patch", "pin", "digest"],
+      "matchCurrentVersion": "!/^0/",
+      "automerge": true
+    }
+  ]
+}

9-data-analysis-python/2-demographic-data-analyzer/test_module.py

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+import unittest
+import demographic_data_analyzer
+
+class DemographicAnalyzerTestCase(unittest.TestCase):
+    @classmethod
+    def setUpClass(self):
+        self.data = demographic_data_analyzer.calculate_demographic_data(print_data = False)
+
+    def test_race_count(self):
+        actual = self.data['race_count'].tolist()
+        expected = [27816, 3124, 1039, 311, 271]
+        self.assertCountEqual(actual, expected, msg="Expected race count values to be [27816, 3124, 1039, 311, 271]")
+
+    def test_average_age_men(self):
+        actual = self.data['average_age_men']
+        expected = 39.4
+        self.assertAlmostEqual(actual, expected, msg="Expected different value for average age of men.")
+
+    def test_percentage_bachelors(self):
+        actual = self.data['percentage_bachelors']
+        expected = 16.4 
+        self.assertAlmostEqual(actual, expected, msg="Expected different value for percentage with Bachelors degrees.")
+
+    def test_higher_education_rich(self):
+        actual = self.data['higher_education_rich']
+        expected = 46.5
+        self.assertAlmostEqual(actual, expected, msg="Expected different value for percentage with higher education that earn >50K.")
+  
+    def test_lower_education_rich(self):
+        actual = self.data['lower_education_rich']
+        expected = 17.4
+        self.assertAlmostEqual(actual, expected, msg="Expected different value for percentage without higher education that earn >50K.")
+
+    def test_min_work_hours(self):
+        actual = self.data['min_work_hours']
+        expected = 1
+        self.assertAlmostEqual(actual, expected, msg="Expected different value for minimum work hours.")     
+
+    def test_rich_percentage(self):
+        actual = self.data['rich_percentage']
+        expected = 10
+        self.assertAlmostEqual(actual, expected, msg="Expected different value for percentage of rich among those who work fewest hours.")   
+
+    def test_highest_earning_country(self):
+        actual = self.data['highest_earning_country']
+        expected = 'Iran'
+        self.assertEqual(actual, expected, "Expected different value for highest earning country.")   
+
+    def test_highest_earning_country_percentage(self):
+        actual = self.data['highest_earning_country_percentage']
+        expected = 41.9
+        self.assertAlmostEqual(actual, expected, msg="Expected different value for highest earning country percentage.")   
+
+    def test_top_IN_occupation(self):
+        actual = self.data['top_IN_occupation']
+        expected = 'Prof-specialty'
+        self.assertEqual(actual, expected, "Expected different value for top occupations in India.")      
+
+if __name__ == "__main__":
+    unittest.main()